Suppressing Interfacial Recombination with a Strong‐Interaction Surface Modulator for Efficient Inverted Perovskite Solar Cells
Bibliographic record
Abstract
Abstract Successful manipulation of halide perovskite surfaces is typically achieved via the interactions between modulators and perovskites. Herein, it is demonstrated that a strong‐interaction surface modulator is beneficial to reduce interfacial recombination losses in inverted (p‐i‐n) perovskite solar cells (IPSCs). Two organic ammonium salts are investigated, consisting of 4‐hydroxyphenethylammonium iodide and 2‐thiopheneethylammonium iodide (2‐TEAI). Without thermal annealing, these two modulators can recover the photoluminescence quantum yield of the neat perovskite film in contact with fullerene electron transport layer (ETL). Compared to the hydroxyl‐functionalized phenethylammonium moiety, the thienylammonium facilitates the formation of a quasi‐2D structure onto the perovskite. Density functional theory and quasi‐Fermi level splitting calculations reveal that the 2‐TEAI has a stronger interaction with the perovskite surface, contributing to more suppressed non‐radiative recombination at the perovskite/ETL interface and improved open‐circuit voltage (VOC) of the fabricated IPSCs. As a result, the VOC increases from 1.11 to 1.20 V (based on a perovskite bandgap of 1.63 eV), yielding a power conversion efficiency (PCE) from ≈20% to 21.9% (stabilized PCE of 21.3%, the highest reported PCEs for IPSCs employing poly[N,N′′‐bis(4‐butylphenyl)‐N,N′′‐bis(phenyl)benzidine] as the hole transport layer, alongside the enhanced operational and shelf‐life stability for unencapsulated devices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".